论文标题
ICFHR 2020竞赛在历史手写片段的图像检索上
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments
论文作者
论文摘要
该竞赛在作家和历史文档图像的样式分析方面取得了成功。特别是，我们研究了大规模检索历史文档片段的表现，从风格和作家身份识别方面。对历史碎片的分析是训练有素的人文主义者通常解决的艰巨挑战。与以前的比赛相比，我们通过解决样本粒度问题并从作者到页面片段检索来使结果更有意义。两种方法（样式和作者身份）提供了有关每种方法哪种信息的信息，可以更好地利用并间接地有助于参与方法的解释性。因此，我们创建了一个由超过12万个片段组成的大数据集。尽管大多数团队基于卷积神经网络提交了方法，但获胜条目的地图达到了40％以下。
This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide information on what kind of information each method makes better use of and indirectly contribute to the interpretability of the participating method. Therefore, we created a large dataset consisting of more than 120 000 fragments. Although the most teams submitted methods based on convolutional neural networks, the winning entry achieves an mAP below 40%.
